The classification of vehicles in moving traffic has a wide spectrum of applications. Automatic rough classification for differentiating large, slower vehicles (trucks, busses) from smaller, faster vehicles (passenger cars) is particularly important in the context of automated monitoring and control of road traffic. According to the class of vehicle detected, for example, different tolls can be charged, traffic light installations can be controlled, or traffic violations can be penalized based on vehicle classes.
In the methods for rough classification of vehicles known from the prior art, vehicles are often classed by determining individual vehicle length based on the entry and exit of a vehicle into and from the measuring zone of a measuring arrangement. A feature of vehicle length which can be evaluated and which allows the vehicle to be assigned to the class of busses and trucks or to the class of passenger cars can be generated from the received measurement signals with sufficient certainty by means of evaluation methods. Known arrangements for this purpose work either with induction loops, which perform the classification based on the length and ground clearance of the vehicle determined when the vehicle drives through, or with radar devices which perform the classification based on the passage of the vehicle through the cone of the radar beam (radar cone) by means of a vehicle length that is determined from the duration of passage and the speed.
In a method disclosed in Laid Open Application EP 1 990 654 A1, the vehicle length is determined by means of a radar device which is installed next to the roadway at an acute angle to the edge of the roadway. Based on distance points which are determined from the entry and exit of the vehicle and on the known installation angle, the length of the stretch of road traveled by the vehicle through the radar cone can be determined. The total distance covered by the vehicle within this time can be determined from the detected vehicle speed and transit time. Accordingly, the vehicle length can be calculated from the difference between the total distance and the transit distance, and the detected vehicle can be classified by comparison with the vehicle lengths typical of a class. Error effects resulting from one vehicle being concealed by another cannot be remedied with this method.
In a method described in Laid Open Application DE 38 10 357 A1, classification is likewise carried out based on the detected vehicle length. For this purpose, a Doppler echo is initially received during passage of a vehicle through the radar cone of a radar device, and the frequency is determined with maximum amplitude from the frequency spectrum of this Doppler echo. A speed is determined based on this frequency. The vehicle length can then be determined from the speed and the signal duration of the Doppler echo. The measurement of vehicle length by signal duration entails a number of error influences. Due to the fact that the radar radiation is reflected by a surface whose size depends on the length of the vehicle, the signal duration is detected in such a way that it is distorted by the vehicle length on principle. Further, the radar beam which is directed obliquely on the vehicles causes shadowing on parts of the vehicles which results in a distorted length measurement. A correction factor which is determined separately and empirically for each influencing variable is used to increase the accuracy of length measurement. Finally, the classification is carried out by comparing the corrected time curve of the Doppler echo with that of stored, identified models. However, the determined vehicle length is ultimately only a very rough estimation which can easily lead to erroneous classifications.
A possibility for classifying vehicles without direct detection of vehicle length is described in patent document EP 2 011 103 B1. A radar beam is aligned along a traffic route by a radar device. A linear frequency modulated CW radar device is used allowing speeds and distances of traffic participants to be determined. The signals reflected by the traffic participants are separated from noise and evaluated in a data processing device. As a result of this processing, two Fourier transformation coefficients are generated; one coefficient is a function of the distance of the detected traffic participants and the other coefficient is a function of the speed of the detected traffic participants. When the two functions are plotted versus one another in the two dimensions of a Cartesian graph, characteristic patterns emerge for the different traffic participants. The composition and shape of these patterns is a measurement of the spread of the speeds and distances of the reflected signals of a traffic participant, the statistical evaluation of which allows traffic participants to be assigned to predefined classes. However, because of the measuring principle of the linear frequency modulated CW radar device, no angles can be associated with acquired objects. Therefore, while it is possible to ascertain the presence, e.g., of a passenger car or a truck in the radar cone, this assertion cannot be assigned with certainty insofar as there is more than one traffic participant located within the radar cone at the same time.